Amin, MiriamMiriamAminBergmann, Jan-PeterJan-PeterBergmannCampbell Borges, Yuri CassioYuri CassioCampbell Borges2023-01-022023-01-022022https://publica.fraunhofer.de/handle/publica/4304672-s2.0-85139590648Science, Technology and Innovation (ST&I) companies as well as large research organizations are repeatedly facing the problem of matching an emerging task with the appropriate skill that is present somewhere in an organizational unit. Many organizations already have skill or competence taxonomies that can be useful in this regard. In this working paper, we present our experiments on automatically recommending suitable skills from the internal skill taxonomy of the Fraunhofer Society research organization to incoming research requests in order to support human decision making processes. We applied three different vector-based approaches for this end, one based on language models, one on word embeddings and one on a simple one-hot-encoding of keywords. Our results show that the language-model-based approach outperforms the other methods and is able to recommend skills to research requests with an MAP of 0.82. These first findings pave the way for further improvements of our method and for the transfer to other related problems.enRecommender SystemsKnowledge ManagementSkill TaxonomyCompetence TaxonomyTask-Skill Matchingconference paper